Earticle

초록

영어

Besides unsupervised feature, universality serves as another important factor determining the practical value of attack detection technology. Considering the difficulty of possessing both features for the existing attack detection techniques, this paper reveals the latent factors invoking missing ratings under the non-random-missing mechanism and further combines these latent factors with Dirichlet process in the framework of probabilistic generative model, thus proposes the Latent Factor Analysis for Missing Ratings(LFAMR)model. Based on performing user clustering with this model, this paper achieves the goal of attack detection by presenting the method for identifying attack cluster in ideal situation. Experimental results show that comparing with the existing detection techniques, LFAMR is more universal and unsupervised, and it can effectively detect shilling attacks of typical types and their derivatives even in lack of the apriori inputs such as user cluster numbers.

목차

Abstract
 1. Introduction
 2. Related Concepts
  2.1 The Data not Missing Mode
  2.2. The Dirichlet Process
 3. Analysis of Potential Factors Score Model
  3.1 Lack of Scoring Potential Factors
  3.2. The Formal Description of LFAMR
 4. Experiment Design and Discussion
  4.1. Data Sets and Experimental Setup
  4.2. Shilling Attack Detection Example
  4.3. The Detection Results Analysis
 Conclusion
 References

키워드

저자

• Man Li [ Shandong Huayu University of Technology, Dezhou 253034, china ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605